The researchers will develop new simulation optimization algorithms based on different sequences of the so-called "reference distributions" in a recently developed approach called model reference adaptive search, and new hybrid global-local search algorithms integrating local gradient search and problem structure. They also will conduct rigorous theoretical analysis of the resulting algorithms, both finite-time behavior using an adaptive search framework and asymptotic behavior using a novel connection to stochastic approximation methods.

In addition, Fu and Marcus will develop efficient computational selection methods for implementing these algorithms in simulation optimization, where the objective function requires multiple simulation replications, which are computationally expensive, in order to estimate system performance. A wide variety of applications from supply chain management to financial engineering will be tested to investigate specific gradient search algorithms and problem structure, and evaluating the effectiveness in terms of empirical behavior.

Simulation is used throughout the US industry, so if successful, the resulting optimization algorithms will have broad practical applicability. This line of research fills an important part of the "analytics" computational tool kit that has led to increased competitiveness for US businesses from manufacturers and retailers with global supply chains to financial services managing complex risk factors.